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+# Agent Simulator
+
+The Agent Simulator allows you to simulate and test agent behaviors by mocking tool outputs and injecting faults (latency, errors) without invoking real tools.
+
+## Key Features
+- **Tool Mocking**: Define mock strategies (Tool Spec or Tracing) to generate tool responses.
+- **Fault Injection**: Inject latency, errors, or custom responses with defined probabilities.
+- **Connection Analysis**: Automatically analyze tool connections using an LLM.
+
+## Automatic Connection Analysis
+The Agent Simulator uses an LLM to analyze the schemas of your tools and identify "stateful parameters" (e.g., IDs created by one tool and used by another). This allows the simulator to maintain a consistent state across tool calls.
+
+- **Creating Tools**: Tools that generate new resources (e.g., `create_ticket`) will have their output captured.
+- **Consuming Tools**: Tools that operate on resources (e.g., `get_ticket`) will be validated against the captured state.
+
+## Configuration
+The Agent Simulator is configured using three main classes: `AgentSimulatorConfig`, `ToolSimulationConfig`, and `InjectionConfig`.
+
+- `AgentSimulatorConfig`: The main configuration object for the Agent Simulator. It holds a list of `ToolSimulationConfig` objects and global settings like the simulation model.
+- `ToolSimulationConfig`: Defines the simulation behavior for a specific tool, including its name, a list of `InjectionConfig` objects, and a mock strategy.
+- `InjectionConfig`: Specifies the fault injection parameters for a tool, such as the probability of injection, latency, and the specific error or response to inject.
+
+### Example Configuration
+```python
+from google.adk.tools.agent_simulator.agent_simulator_config import AgentSimulatorConfig, ToolSimulationConfig, InjectionConfig, MockStrategy, InjectedError
+
+config = AgentSimulatorConfig(
+    tool_simulation_configs=[
+        ToolSimulationConfig(
+            tool_name="my_tool",
+            injection_configs=[
+                InjectionConfig(
+                    injection_probability=0.5,
+                    injected_error=InjectedError(
+                        injected_http_error_code=500,
+                        error_message="Internal Server Error"
+                    )
+                )
+            ],
+            mock_strategy_type=MockStrategy.MOCK_STRATEGY_TOOL_SPEC
+        )
+    ]
+)
+```
+
+## Usage
+You can integrate the Agent Simulator into your workflow as a Plugin or a Callback.
+
+### Using as a Plugin
+To use the Agent Simulator as a plugin, create a plugin instance using `AgentSimulatorFactory.create_plugin(config)` and pass it to the runner.
+
+```python
+from google.adk.tools.agent_simulator.agent_simulator_factory import AgentSimulatorFactory
+from google.adk.runners import InMemoryRunner
+
+plugin = AgentSimulatorFactory.create_plugin(config)
+runner = InMemoryRunner(agent=agent, plugins=[plugin])
+```
+
+### Using as a Callback
+To use the Agent Simulator as a callback, create a callback function using `AgentSimulatorFactory.create_callback(config)`. This can be used as a `before_tool_callback` or `after_tool_callback`.
+
+```python
+from google.adk.tools.agent_simulator.agent_simulator_factory import AgentSimulatorFactory
+
+callback = AgentSimulatorFactory.create_callback(config)
+# Use the callback in your tool setup
+```